Without limiting the scope of the invention, its background is described in connection with data and computer communications.
The 1970's and early 1980's saw a merger of the fields of computer science and data communications that profoundly changed the technology, products and companies of the now combined computer-communications industry.
Computers and other electronic equipment need a method of connection and method of communicating in order to transfer information back and forth. The method of connection is referred to as a communications network architecture or topology. The method of communicating is referred to as a communications protocol. There are many existing methods of communicating and also many methods of connecting computer and electronic devices. But, these methods allow only a few devices to talk at any given time. This means that these devices have to take turns to communicate to each. These existing solutions to the data communications problems are limiting the present data and computer technologies.
In addition, the data and computer communications field is an ever developing field. With computer technology rapidly advancing, the requirements of communications networks are also growing by leaps and bounds. Ethernet, token ring and other existing local area network protocols will not be able to meet the needs of future data and computer communications. The advent of graphic processing such as Xwindows and imaging servers is stretching the existing technologies to their limits. The emergence of new medias, such as fiber optics, is only part of the solution that is needed to respond to demands of these and other not yet developed technologies. In addition to new media, a faster communications protocol and network architecture is also needed to process tomorrow's communication needs.
In addition, the rapidly developing field of parallel processing is also a computer field that requires many computing elements to communicate with each other at the same time. Neural networks are a subdivision of parallel processing. Neural networks often have many processing units (e.g. microprocessors) that require constant communication. The problems and solutions to the architecture and topology of computer networks may sometimes be adaptable to neural networks, since they are in a sense, smaller computer networks within a larger computer.